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library(dplyr)
library(readr)
library(spatstat)
library(ggExtra)

df<-read_csv("H07-0500_79205589_179_MBP.csv")

Rows: 3604 Columns: 4
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): roi_source
dbl (3): com_x, com_y, pixel_area

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

a<-ggplot(df_full,
   aes( com_x,  com_y,size=pixel_area))+ geom_point(shape = ".")+xlab("x position in pixel")+ylab("y position in pixel")+theme_bw()+geom_jitter(width = 0, height = 0.001, alpha = 0.2)#+ theme(aspect.ratio = )

b<-ggMarginal(a,type="histogram",fill = "lightgrey",binwidth=20)
b

ln = with(df_full,
  ppp(x = com_x, y = com_y, marks = pixel_area, xrange = range(com_x), yrange = range(com_y)))
qq=quadratcount(ln,nx=3,ny=12)
plot(qq,main='')

quadrat.test(qq)

    Chi-squared test of CSR using quadrat counts

data:  
X2 = 483.24, df = 35, p-value < 2.2e-16
alternative hypothesis: two.sided

Quadrats: 3 by 12 grid of tiles

d = density(ln, edge=TRUE, diggle=TRUE,sigma=150)
plot(d)

x<- df$com_y
y <- df$com_x
library(MASS)
ln_3d <- kde2d(x, y)

persp(ln_3d, box=FALSE)

library(plotly)
plot_ly(x=ln_3d$x, y=ln_3d$y, z=ln_3d$z) %>% add_surface()

eG = envelope(ln, fun=Gest, nsim=200,nrank=1)

Generating 200 simulations of CSR  ...
1, 2, 3, 4.6.8.10.12.14.16.18.20.22.24.26.28.30.32.34.36.38.40
.42.44.46.48.50.52.54.56.58.60.62.64.66.68.70.72.74.76.78.80
.82.84.86.88.90.92.94.96.98.100.102.104.106.108.110.112.114.116.118.120
.122.124.126.128.130.132.134.136.138.140.142.144.146.148.150.152.154.156.158.160
.162.164.166.168.170.172.174.176.178.180.182.184.186.188.190.192.194.196.198. 200.

Done.

plot(eG)